Cyber intelligence system and method

ABSTRACT

Aspects of the subject disclosure may include, for example, a device that includes a processing system including a processor, and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, where the operations include receiving intelligence requirements from sources authenticated through a blockchain distributed ledger, normalizing data received by the sources, wherein the normalized data complies with the intelligence requirements, and generating a recommendation or course of action based on the normalized data. Other embodiments are disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/019,179 filed on Jun. 26, 2018. All sections of the aforementionedapplication are incorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a system and method convergingblockchain with intelligence data sharing.

BACKGROUND

Top primary commercial sector strategic leader information needs aremost commonly: 1. Risk of bad public relations; 2. Negative short-termor long-term risks to the bottom line; or 3. Substantiated comparison toindustry competitors. Capabilities that answer strategic leader needswith timely and actionable terms guarantee absolute relevance.

Strategic leader concerns are fundamentally answered on a day-to-daybasis by Chief Officers from different internal business units,including Security, Risk and Compliance, and Finance. Strategiccollection and reporting in most commercial organizations occur inisolation “silos.” Organization sociocultural and subculture routinesfurther decline business agility and inhibit rapid information flow.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein;

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a cyber intelligence system functioning within thecommunication network of FIG. 1 in accordance with various aspectsdescribed herein;

FIG. 2B depicts an illustrative embodiment of a method performed by acyber intelligence system in accordance with various aspects describedherein;

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein;

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein;

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein; and

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for a server suite that processes intelligence data andrequirements, and produces intelligence reports. Other embodiments aredescribed in the subject disclosure.

One or more aspects of the subject disclosure include a device thatincludes a processing system including a processor, and a memory thatstores executable instructions that, when executed by the processingsystem, facilitate performance of operations, where the operationsinclude receiving intelligence requirements from sources authenticatedthrough a blockchain distributed ledger, normalizing data received bythe sources, wherein the normalized data complies with the intelligencerequirements, and generating a recommendation or course of action basedon the normalized data.

One or more aspects of the subject disclosure include a machine-readablemedium, comprising executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations, the operations comprising: authenticating sources using acode chain stored in a distributed blockchain ledger, thereby creatingauthenticated sources, wherein the authenticated sources are graded byreliability; receiving information from an authenticated source in theauthenticated sources, wherein and the information is graded bycredibility; normalizing the information to comply with intelligencerequirements; and generating a recommendation or course of action basedon the normalized information.

One or more aspects of the subject disclosure include a method,comprising: authenticating, by a processing system including aprocessor, sources through a blockchain distributed ledger; receiving,by the processing system, intelligence requirements from the sources;normalizing, by the processing system, data received from the sources,wherein the normalized data complies with the intelligence requirements;and generating, by the processing system, a recommendation or course ofaction based on the normalized data.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. In particular, acommunications network 125 is presented for providing broadband access110 to a plurality of data terminals 114 via access terminal 112,wireless access 120 to a plurality of mobile devices 124 and vehicle 126via base station or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communication network 125 is coupled to one or morecontent sources 175 of audio, video, graphics, text and/or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device (e.g., mobile devices 124 can receive media content viamedia terminal 142, data terminal 114 can be provided voice access viaswitching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a cyber intelligence system 200 functioning within thecommunication network of FIG. 1 in accordance with various aspectsdescribed herein. As shown in FIG. 2A, cyber intelligence system 200includes a related series of server suites that perform automatedlogic-control tasks and artificial cognitive functions, normallyrequiring human intelligence and interaction. By processing data withoutfull context (information) using Natural Language Processing (NLP) andMachine Learning (ML), system 200 produces data with full context(intelligence), thus automating risk, threat, and course of actionanalysis (at machine speed) that would otherwise be impossible toaccomplish anthropomorphically (at human speed). In an embodiment,system 200 reduces national risks and threats in the cyber domain usingthe herein described technology and automation. Such processing bysystem 200 enhances full-context data delivery (intelligence) used incyber risk and threat decision-making. Further, system 200 improvescollaboration, knowledge, and capabilities in public-private cyber riskand threat operations.

The three server suites implemented by the series of engines comprisesan ingestion engine 220, a model engine 230, and an analyzer engine 240(collectively, server engines). Ingestion engine 220 consumes data 211,comprising unstructured (unnormalized) and structured information fromall available sources that is generally received over the Internet 210,requests 212 for information or support, and requirements 213.Generally, requests 212 and requirements 213 are structured data,whereas data 211 may or may not be structured. Data 211 and requests212, such as petitions for available information about a topic orsubject or petitions for assistance in an action related to a topic orsubject, are usually related to an intelligence requirement 213, whichis a topic or subject, as set forth below.

Unstructured data 211 is also known as data without full context. Data211 may comprise open sources, closed sources and direct sources. Opensources can be any publicly available source, and typically comprisesunstructured data. Closed sources are any resource that is not availableto the public as a closed access resource, and typically comprisessemi-structured data. Direct sources are any resource that is explicitlyrestricted, or otherwise produced with limited accessibility capacity,and typically comprises well-structured data.

The system 200 grades the sources of data 211 based on reliability as atechnical assessment of the capability of the source. Sources aregenerally rated for reliability on a grading scale from “A” to “E,”where “A” is reliable and “E” is unreliable, based on previous reportingfrom the source. By default, a source is rated “F” if there is noprevious reporting, to indicate that there is insufficient informationto evaluate reliability of the source.

Data 211, or information provided by a source is graded by credibilityin the likelihood and levels of corroboration by other sources.Information is generally rated for credibility on a scale from “1” to“5,” where “1” indicates that the information is logical, consistentwith other relevant information, and confirmed by plural independentsources, and “5” indicates the information is not logical and iscontradicted by other relevant information. Information that iscompletely new, where the validity of the information cannot bedetermined, is given a rating of “6.”

Intelligence requirements 213 are compulsory demands by leadershipstipulated at the appropriate echelon of an organization. Requirements213 exist in organizational context to collect and answer leadershipmanifested risk and threat concerns, to remain operationally relevant.Ideally, each Chief Officer (Security, Financial, Risk & Compliance,etc.) of a hierarchical organization should consider higher level (i.e.,CEO/President/Board) echelon concerns, and then develop a business unitspecific set of operational intelligence requirements 213. Anintelligence requirement produces a need for collecting actionableinformation in any subject, general or specific, which producesintelligence. Exemplary intelligence requirements include: identifyingthe person or group responsible for hostile action; determining a motivebehind the hostile action (criminal, political, espionage, or other);identifying the person, group, or entity subject to the hostile action;safeguards or countermeasures to avoid, detect, counteract, or minimizerisks and threats associated with the hostile action; and identifyingconnections to organizational strategic risks, such as bad publicrelations, short-term and long-term impacts to the bottom line, industryperformance comparison, etc.

Ingestion engine 220 processes sources using a variety of strategiesincluding Natural Language Processing (NLP), public schemas, ad hocdiscovery, etc., to normalize the data, resulting in processed sources.Normalization restructures relational information to conform to astandard that reduces data redundancy, improves data integrity, andincreases transaction rates. In an embodiment, normalization creates arecord in a database 222 for each source that includes the followingfields:

-   -   UID—Unique data record (line) identifier, or article reference        number    -   TYPE—Common object category (i.e., Actor, Malware)    -   NAME—Common object designation or name (i.e., fuzzy-bear,        almawt-hackers, backorifice, bifrost, etc.)    -   ALIAS—Familiar name(s) associated with object from all sources    -   LOCATION—Geopolitical boundary of actor/group activity (not        necessarily physical location of article or indicator) (i.e.,        World, Continent, Country, Region, State, City, Local/Tribe)    -   MOTIVE—Explicit (Criminal, Political, Espionage, or Other)        actor/group estimated motive(s)    -   RISK—Request or requirement reference, addressing strategic risk        (“above C-level”) first, then tactical threats (“at C-level or        below”)    -   COMPENSATING CONTROL—Freeform (from a defined list) security-led        operations existing, emergent, or recommended physical or        logical risk/threat compensating control(s)    -   CONFIDENCE—Source confidence rating (see above “Source        Reliability (A-F rating) and Information Credibility (1-6        rating)”)    -   FSEEN—Date/Time of first seen activity (GMT)    -   LSEEN—Date/Time of last seen activity (GMT)    -   TAG—Standardized tagging for sorting, searching, and data        distribution    -   NOTE—Freeform (no format) text field    -   ATTACHMENT—Object or link extension (actual article/object or        referrer)

Next, ingestion engine 220 generates graph statements 223 from theprocessed sources for use in modeling and analysis. A graph statement223 is a definition of a node or edge of the graph containing itsidentity, feature and value. The identity of a graph node is a uniquename for the node that can be used as a reference for the node. Thegraph node's feature and value are aspect of the node that havesignificance in the context of the graph. For example, a graph statementasserting a website is malicious may have the features and value aboutthe time of detecting the malicious website, hashes of malicious filesfound on the malicious website, etc. Certain data transformations takeplace during the generation of graph statements 223 by the ingestionengine 220. These data transformation include: transforming availablestatements, originating from unnormalized data 211 and/or requests 212,into an assertion statement, which is a confidence based statement abouta presumed threat or risk used for construction of the graph. Theassertion statements are checked for quality, pedigree, and integrity.Simple data quality steps, such as confirming the existence of thesubject of the statement, authenticating sources, and checking changehistory are used for data assurance. If the assertion statement is foundto be satisfactory, then it is transformed into a graph statement 223.

As ingestion engine 220 parses structured intelligence requirements 213for normalization, ingestion engine 220 decides whether to accept thestructured requirement 213 or not. If ingestion engine 220 acceptsstructured intelligence requirements 213, then it updates a code chain224 and creates analytic objects. Code chain 224 is an application ofdistributed ledger technology that employs traditional blockchain andexternal smart contract techniques to provide access and provenanceusing Private Keyed Infrastructure (PKI) based encryption and accesscontrol, without a requirement for a third party certificate authority.The code contained in code chain 224 implements and enforces businesslogic for access control and prerequisites used to maintain thelifecycle of the intelligence.

Code chain 224 enforcement of prerequisites and lifecycle maintenanceare complex logic blocks that ensure that authorized contributors havesatisfied entry conditions and have applied proper operations and dataelements applicable to the intelligence artifact. For example, when anassertion of a threat or risk is first made, the existence of astatement identifying the same threat or risk cannot already exist.Furthermore, contributors that make assertions must be authorized forsuch operations—thus, the code chain 224 ensures that the operation isperformed by an appropriate party, and in the correct order.

Public and private collaboration in the form of shared intelligenceinformation can help detect, prevent, or mitigate known or suspectedhostile intentions or actions. Intelligence consumers who read/write tothe distributed ledger and/or smart contracts must have the properaccess credentials, and must be granted the proper operationpermissions, by code chain 224, based on the state of the intelligenceproduct. For example, a consumer who tries to operate on an intelligenceproduct that is in an unfinished state will be denied access to write tothe distribute ledger for operations that require finished products.

Code chain 224 transforms credentials into a set of permissions andencryption measures that provide opportunities for ledger entries. Codechain 224 transforms submitted operations (i.e., assertions,intelligence product, etc.) into ledger entries according to logicallowed by code elements of code chain 224. For example, only users withproper permissions for statements that are in the assertion state, asdescribed above may provide corroboration entries, which are additionalstatements that strengthen the declaration of a threat or risk.Furthermore, code chain 224 examines credentials of entities that seekto transform submitted operations into ledger entries, and permits suchtransformation according to logic in code chain 224.

Analytic objects are a detailed dissection of an object or an idea arederived from normalizing data placed in database 222. Data 211 withoutfull context exists to this point as information that has beennormalized and recorded in database 222. Model engine 230 appliesnormalized information and turns it into data with full context known asintelligence 231.

Model engine 230 consumes normalized structured data (such as graphstatements 223, data 211, requests 212, and requirements 213) stored indatabase 222. Model engine 230 organizes information from sources thatmeet structured requirements into risk and threat assessments for use byanalyzer engine 240. During this organization process, model engine 230provides graph constructs, or nodes and edges which represent thestructured intelligence requirements 213 and other information relatedto it, such as its risk elements, sources, etc. The resulting graphstatements 223, fixated on the structured intelligence requirements 213,are used in processing subsequent analytic results. Model engine 230also creates intelligence statements 232, which are answers to theintelligence requirements 213, and organizes the intelligence statements232 into a global logical graph 233.

Global logical graph 233 represents the entire real-world graph ofelements (actors, resources, relationships, etc.) associated withintelligence, primarily in the form of a neural network scheme. A neuralnetwork helps solve the complex many-to-many relationships whileproviding direct access (i.e. random access) to the massive scale ofsuch a global graph. Model engine 230 provides updates to code chain224, as available and appropriate for use in the global logical graph233 during lifecycle transactions and retrieves sub-graphs.

Lifecycle transactions are a systematic series of changes during routinefunctioning of system 200. Model engine 230 updates the lifecycletransactions in operations that define the status and state of theintelligence (e.g. create, update, delete, finished, in-progress, etc.)Model engine 230 provides interfaces and methods for retrievingsub-graphs, which are specific portions of the global logical graph 233that are relevant to analytical operations. Such sub-graphs are theproduct of direct access of the global logical graph 233 which meet asearch or filtering criteria for a specific analysis operation (e.g.geographical filter, sub-net mask, etc.). Model engine 230 transformsthat collection of graph statements 223 into a logical organization ofresources and facts about the resources, including an identification ofthe resource, the source providing the resource, assertions about theresource, corroborating evidence, relationship to other resources, etc.Model engine 230 submits code chain entries concerning the existence andstate of graph statements 223, particularly as the graph statements 223change the state of the subject (i.e., threat actor or risk element).

Analyzer engine 240 retrieves all relevant intelligence 231, expressedby intelligence statements 232, and/or realized in the global logicalgraph 233, for analysis and further prioritization based on leadershipobjectives, then updates the topic or subject related code chain 224.Structured intelligence requirements 213 are prioritized according tosuch factors as leadership determined risks, threat relevance, andconfidence rating to an organization whose objectives will determinehow, when and why analysis is performed.

Analyzer engine 240 retrieves sub-graphs and posts updates to code chain224 during any confirmed change to the data hash in the previous chainblock, as confirmed by authorized collaborative resource. Collaborativeresources are role-based operations inclusive of analysts, data brokers,and consumers. Analyzer engine 240 evaluates sub-graph events andinformation surfaces as the entire spectrum of an organization'sexposure to danger, to progressively improve performance in intelligencetasks. Machine learning techniques such as classification, anomalydetection, decision trees, deep learning, etc. are used in conjunctionwith pattern analysis to provide predictive analytics in risk and threatassessment. Analyzer engine 240 converts predictive results into graphstatements 223 that feed requesting sub-graphs of the global logicalgraph 233. This provides intelligence 231 for graphs and intelligencestatements 232 with increased analytic value. Code chain 224 isappropriately updated with consumable intelligence supported bysubstantiated evidence in the data.

Analyzer engine 240 applies human responses (e.g., approve/disapprove)to the graphs and context rich text intelligence reports 245, realizedin a review/feedback cycle of request and requirement products, usingMachine Learning (ML) to form a bias that influences future collection,correlation, distribution, and natural language processing (NLP)selection by the system. The system will tailor the intelligencestatements 232 specifically to user preferences and/or behaviors. Forexample, such users include pro-risk or risk-adverse decision-makers(primary users), or risk-versus threat context for tactical-level users(secondary users, such as analysts). A feedback loop in machine-speedlearning algorithms provide an automated capability to learn and improvefrom experience without being explicitly programmed. As more distributedseries of engines are connected through the use of code chain and moredata is collectively processed for intelligence value, the machine growsand operates as an artificial neural network.

Analyzer engine 240 performs the bulk of the data transformation ofsystem 200. Analyzer engine 240 transforms corroboration into concisemeasurements of risk, priority, and certainty, etc. In an embodiment, aconcise measurement for risk is the product of the probability of theevent occurring times the impact, wherein the measurement identifiespotential hazards and analyzes what could happen if a hazard occurs.Priority is based on executive established (intelligence) requirements,prioritized by an operational echelon (strategic, operational,tactical), that are critical to accomplishing the mission or objective.Certainty is an objective, factual, definite truth (or confidence)rating that is measurable as a metric in percentile (related to theAdmiralty Code; intelligence rating system previously referenced;example A1 source, F6 source). These measurements and any state changescreated therefrom are committed in the code chain 224. Analyzer engine240 transforms corroboration of assertion, which is a collection ofsupporting or opposing declarations, into intelligence statements 232that includes tags and/or labels indicating the state of theintelligence statements 232, e.g., initial, in-process, or finished. Anintelligence statement 232 is considered in-process when it does notfully answer an executive intelligence requirement, but is a finishedintelligence product when it fully answers an executive intelligencerequirement. Analyzer engine 240 submits the intelligence statements 232to the global logical graph 233 and to code chain 224 as appropriate torecord the transactional history. Analyzer engine 240 transformsfinished intelligence statements 232 into actionable product(s) andsubmits them to code chain 224 for use and operations. Actionableintelligence products are full context data that can be acted upon, withthe further implication that actions should be taken (for example,recommendations or courses of action (COAs)). Intelligence reports 245address risks (exposure to danger, strategic concerns) at thestrategic-decision maker level and threats (intent or action involvinghostile action, tactical concerns) at the tactical-operator level. Anexample of strategic actionable products are Executive Summaries(EXSUMs) having strategic risk implications. An example of tacticalactionable products are Intelligence Summaries (INTSUMs) having tacticalthreat implications. Analyzer engine 240 transforms all states ofintelligence product(s) into intelligence reports 245 that are providedto the original requesters and other stakeholders involved the entireprocess performed by system 200. Stakeholders involved in the processhave a role-based participant requirement for contribution andconsumption. Exemplary roles include Knowledge Managers, Regulators,Sources, Brokers, and Consumers, where each role acts as a stakeholderand has input in the intelligence content review, contribution, ordistribution.

FIG. 2B depicts an illustrative embodiment of a method performed by acyber intelligence system in accordance with various aspects describedherein. As shown in FIG. 2B, the process begins at step 251, where thesystem authenticates, evaluates, and prioritizes sources. The sourcescan provide data, intelligence requests, intelligence requirements, or acombination thereof. These sources, cyber intelligence lifecycleparticipants (Knowledge Managers, Regulators, Sources, Brokers, andConsumers), must have access to a secure distributed (decentralized)trusted network technology so they may monitor, access, share, andanalyze consistent and up-to-date intelligence specific content,irrespective of where the intelligence product is in the lifecycle.

Next, in step 252, the system receives and processes intelligencerequirements received from sources. The intelligence requirements may beto assess risk or track threats to one or more organizations.

Next, in step 253, the system receives and processes data from sources,authenticated or not, and normalizes the data. The normalized data isstored in a database that facilitates a chain of time-stamped,cryptographically secured, immutable blocks of consensus-validateddigital data, realized in the existence of multiple synchronized,geographically distributed data points.

Finally, in step 254, the system automatically generates recommendationsor courses of action based on the data and requirements. The systemallows data, analytics and analysts from various sources to collaboratemore efficiently, making it more convenient and more accessible to shareor track risks and threats to the organization. This results in astronger organization security posture with enhanced capability inexecutive decision making.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2B, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of system 200, and method 250presented in FIGS. 1, 2A, 2B and 3.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, or server engines 220, 230, 240, etc. For example, the networkarchitecture can provide a substrate of networking capability, oftencalled Network Function Virtualization Infrastructure (NFVI) or simplyinfrastructure that is capable of being directed with software andSoftware Defined Networking (SDN) protocols to perform a broad varietyof network functions and services. This infrastructure can includeseveral types of substrates. The most typical type of substrate beingservers that support Network Function Virtualization (NFV), followed bypacket forwarding capabilities based on generic computing resources,with specialized network technologies brought to bear when generalpurpose processors or general purpose integrated circuit devices offeredby merchants (referred to herein as merchant silicon) are notappropriate. In this case, communication services can be implemented ascloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router, or server engines 220, 230 and 240 (shown in FIG. 2A)can be implemented via a VNE 330 composed of NFV software modules,merchant silicon, and associated controllers. The software can bewritten so that increasing workload consumes incremental resources froma common resource pool, and moreover so that it's elastic: so theresources are only consumed when needed. In a similar fashion, othernetwork elements such as other routers, switches, edge caches, andmiddle-boxes are instantiated from the common resource pool. Suchsharing of infrastructure across a broad set of uses makes planning andgrowing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The VNEs 330, 332 and 334 can employ networkfunction software that provides either a one-for-one mapping oftraditional network element function or alternately some combination ofnetwork functions designed for cloud computing. For example, VNEs 330,332 and 334 can include route reflectors, domain name system (DNS)servers, and dynamic host configuration protocol (DHCP) servers, systemarchitecture evolution (SAE) and/or mobility management entity (MME)gateways, broadband network gateways, IP edge routers for IP-VPN,Ethernet and other services, load balancers, distributers and othernetwork elements. Because these elements don't typically need to forwardlarge amounts of traffic, their workload can be distributed across anumber of servers—each of which adds a portion of the capability, andoverall which creates an elastic function and a distributed processingenvironment with higher availability than its former monolithic version.These VNEs 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, ingestion engine 220,model engine 230, analyzer engine 240 and/or VNEs 330, 332, 334, etc.Each of these devices can be implemented via computer-executableinstructions that can run on one or more computers, and/or incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The internal HDD 414, magnetic FDD 416 and optical disk drive 420can be connected to the system bus 408 by a hard disk drive interface424, a magnetic disk drive interface 426 and an optical drive interface428, respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. In one or more embodiments, the mobilenetwork platform 510 can generate and receive signals transmitted andreceived by base stations or access points such as base station oraccess point 122. Generally, mobile network platform 510 can comprisecomponents, e.g., nodes, gateways, interfaces, servers, or disparateplatforms, that facilitate both packet-switched (PS) (e.g., internetprotocol (IP), frame relay, asynchronous transfer mode (ATM)) andcircuit-switched (CS) traffic (e.g., voice and data), as well as controlgeneration for networked wireless telecommunication. As a non-limitingexample, mobile network platform 510 can be included intelecommunications carrier networks, and can be considered carrier-sidecomponents as discussed elsewhere herein. Mobile network platform 510comprises CS gateway node(s) 512 which can interface CS traffic receivedfrom legacy networks like telephony network(s) 540 (e.g., publicswitched telephone network (PSTN), or public land mobile network (PLMN))or a signaling system #7 (SS7) network 560. CS gateway node(s) 512 canauthorize and authenticate traffic (e.g., voice) arising from suchnetworks. Additionally, CS gateway node(s) 512 can access mobility, orroaming, data generated through SS7 network 560; for instance, mobilitydata stored in a visited location register (VLR), which can reside inmemory 530. Moreover, CS gateway node(s) 512 interfaces CS-based trafficand signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTSnetwork, CS gateway node(s) 512 can be realized at least in part ingateway GPRS support node(s) (GGSN). It should be appreciated thatfunctionality and specific operation of CS gateway node(s) 512, PSgateway node(s) 518, and serving node(s) 516, is provided and dictatedby radio technology(ies) utilized by mobile network platform 510 fortelecommunication over a radio access network 520 with other devices,such as radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WAN) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WAN 550 and enterprise network(s) 570 can embody, at least inpart, a service network(s) like IP multimedia subsystem (IMS). Based onradio technology layer(s) available in technology resource(s) of radioaccess network 520, PS gateway node(s) 518 can generate packet dataprotocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication of unnormalized data 211, intelligence requests 212,or intelligence requirements 213, via either communications network 125or Internet 210.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1×, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

Some of the embodiments described herein can also employ artificialintelligence (AI), Machine Learning (ML) and related necessary “trainingdata”; where the application and advent of subsection (ML) functions inDeep Learning (DL) does not require training data and serves as theartificial neural network basis of the current system 200 and nextevolution inclusive of artificial cognitive decision logic modules tofacilitate automating one or more features described herein. See, e.g.,https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/,which is incorporated by reference herein. The embodiments (e.g., inconnection with automatically identifying acquired cell sites thatprovide a maximum value/benefit after addition to an existingcommunication network) can employ various AI-based schemes for carryingout various embodiments thereof. Moreover, the classifier can beemployed to determine a ranking or priority of each cell site of theacquired network. A classifier is a function that maps an inputattribute vector, x=(x₁, x₂, x₃, x₄ . . . x_(n)), to a confidence thatthe input belongs to a class, that is, f(x)=confidence (class). Suchclassification can employ a probabilistic and/or statistical-basedanalysis (e.g., factoring into the analysis utilities and costs) todetermine or infer an action that a user desires to be automaticallyperformed. A support vector machine (SVM) is an example of a classifierthat can be employed. The SVM operates by finding a hypersurface in thespace of possible inputs, which the hypersurface attempts to split thetriggering criteria from the non-triggering events. Intuitively, thismakes the classification correct for testing data that is near, but notidentical to training data. Other directed and undirected modelclassification approaches comprise, e.g., naïve Bayes, Bayesiannetworks, decision trees, neural networks, fuzzy logic models, andprobabilistic classification models providing different patterns ofindependence can be employed. Classification as used herein also isinclusive of statistical regression that is utilized to develop modelsof priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a hardware processor; and a memory that stores executableinstructions that, when executed by the processing system, facilitateperformance of operations, the operations comprising: authenticatingsources through a blockchain distributed ledger; normalizing datareceived from the sources, thereby forming normalized data, wherein thenormalized data complies with intelligence requirements, and wherein thenormalizing of the data restructures relational information to conformto a standard that reduces data redundancy, improves data integrity, andincreases transaction rates; storing the normalized data in theblockchain distributed ledger; and generating a recommendation based onthe normalized data.
 2. The device of claim 1, wherein the operationsfurther comprise grading the sources based on reliability, credibility,or a combination thereof.
 3. The device of claim 2, wherein theoperations further comprise confirming an existence of a subject of astatement contained in the normalized data.
 4. The device of claim 3,wherein the operations further comprise authenticating the sources. 5.The device of claim 4, wherein the operations further comprise checkinga change history of the normalized data to ensure integrity of thenormalized data.
 6. The device of claim 5, wherein the operationsfurther comprise converting the normalized data into a graph statementcomprising receiving corroboration entries that strengthen a declarationof a threat or risk identified by the normalized data.
 7. The device ofclaim 1, wherein the operations further comprise receiving theintelligence requirements from the authenticated sources.
 8. The deviceof claim 6, wherein the receiving of the corroboration entries are fromthe sources authenticated through the blockchain distributed ledger. 9.The device of claim 8, wherein the processing system comprises aplurality of processors operating in a distributed processingenvironment.
 10. A non-transitory, machine-readable medium, comprisingexecutable instructions that, when executed by a processing systemincluding a processor, facilitate performance of operations, theoperations comprising: authenticating sources using a code chain storedin a distributed blockchain ledger, thereby creating authenticatedsources; receiving information from an authenticated source in theauthenticated sources, wherein and the information is graded bycredibility; normalizing the information to comply with intelligencerequirements, thereby forming normalized information, wherein thenormalizing of the information restructures relational information toconform to a standard that reduces data redundancy, improves dataintegrity, and increases transaction rates; storing the normalizedinformation in the blockchain distributed ledger; and generating arecommendation based on the normalized information.
 11. Thenon-transitory, machine-readable medium of claim 10, wherein theprocessing system comprises a plurality of processors operating in adistributed processing environment, and wherein the normalizingcomprises natural language processing.
 12. The non-transitory,machine-readable medium of claim 11, wherein the operations furthercomprise confirming an existence of a subject of a statement containedin the normalized information.
 13. The non-transitory, machine-readablemedium of claim 12, wherein the operations further comprise checking achange history of the normalized information to ensure integrity. 14.The non-transitory, machine-readable medium of claim 13, wherein theoperations further comprise comprises receiving corroboration entriesthat strengthen a declaration of a risk or threat identified by thenormalized information.
 15. The non-transitory, machine-readable mediumof claim 14, wherein the operations further comprise converting thenormalized information into a graph statement.
 16. A method, comprising:authenticating, by a processing system including a processor, sourcesthrough a blockchain distributed ledger; normalizing, by the processingsystem, data received from the sources, thereby forming normalized data,wherein the normalizing of the data restructures relational informationto conform to a standard that reduces data redundancy, improves dataintegrity, and increases transaction rates; storing the normalized datain the blockchain distributed ledger; and generating, by the processingsystem, a recommendation based on the normalized data.
 17. The method ofclaim 16, comprising: confirming, by the processing system, an existenceof a subject of a statement contained in the normalized data.
 18. Themethod of claim 16, comprising: grading, by the processing system, thesources based on reliability, credibility, or a combination thereof. 19.The method of claim 16, comprising: checking, by the processing system,a change history of the normalized data to ensure integrity of thenormalized data.
 20. The method of claim 16, comprising: receiving, bythe processing system, corroboration entries that strengthen adeclaration of a risk or threat identified by the normalized data.